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Estimated Generalized Least Squares for Random Coefficient Regression Models

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  • V. V. Anh
  • T. Chelliah

Abstract

ABSTRACT. This paper considers a general class of random coefficient regression (RCR) models to represent pooled cross‐sectional and time series data. A new method is given to estimate the covariance matrix of the error component in these RCR models. Also, the asymptotic and small sample properties of the estimated generalized least squares estimator of the regression coefficient vector are established. Procedures for testing a linear restriction on the mean vector of the random coefficients are derived. Finally, a test for non‐randomness in the RCR model is devised, and the asymptotic distribution of the test statistic is obtained.

Suggested Citation

  • V. V. Anh & T. Chelliah, 1999. "Estimated Generalized Least Squares for Random Coefficient Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(1), pages 31-46, March.
    • Handle: RePEc:bla:scjsta:v:26:y:1999:i:1:p:31-46
    DOI: 10.1111/1467-9469.00135
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    Cited by:

    1. Petzold, Max & Jonsson, Robert, 2003. "Maximum Likelihood Ratio based small-sample tests for random coefficients in linear regression," Working Papers in Economics 102, University of Gothenburg, Department of Economics.
    2. Abonazel, Mohamed R., 2016. "Generalized Random Coefficient Estimators of Panel Data Models: Asymptotic and Small Sample Properties," MPRA Paper 72586, University Library of Munich, Germany.

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